


from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCAmelCase_				:			Optional[int]   =	logging.get_logger(__name__)  # pylint: disable=invalid-name
class 			SCREAMING_SNAKE_CASE__  (    __snake_case						,   __snake_case      ):
						@register_to_config
						def __init__(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	bool					,							SCREAMING_SNAKE_CASE__    :	Optional[int] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[int] = None					)		->      str:
													super().__init__()

													a_ :		Tuple							=  learnable

													if self.learnable:
																				assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
																				assert length is not None, "learnable=True requires `length` to be set"

																				a_ :		str							=  torch.zeros(lowerCamelCase_					,							lowerCamelCase_					)
													else:
																				a_ :		Any							=  None

													a_ :		Optional[int]							=  torch.nn.Parameter(lowerCamelCase_					)
class 			SCREAMING_SNAKE_CASE__  (    __snake_case      ):
						snake_case__    :					VQModel
						snake_case__    :					CLIPTextModel
						snake_case__    :					CLIPTokenizer
						snake_case__    :					TransformeraDModel
						snake_case__    :					LearnedClassifierFreeSamplingEmbeddings
						snake_case__    :					VQDiffusionScheduler
						def __init__(	self    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	VQModel					,							SCREAMING_SNAKE_CASE__    :	CLIPTextModel					,							SCREAMING_SNAKE_CASE__    :	CLIPTokenizer					,							SCREAMING_SNAKE_CASE__    :	TransformeraDModel					,							SCREAMING_SNAKE_CASE__    :	VQDiffusionScheduler					,							SCREAMING_SNAKE_CASE__    :	LearnedClassifierFreeSamplingEmbeddings					,							)		->      Tuple:
													super().__init__()

													self.register_modules(
													    vqvae=lowerCamelCase_					,							transformer=lowerCamelCase_					,							text_encoder=lowerCamelCase_					,							tokenizer=lowerCamelCase_					,							scheduler=lowerCamelCase_					,							learned_classifier_free_sampling_embeddings=lowerCamelCase_					,							)
						def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					)		->      Optional[int]:
													a_ :		Union[str, Any]							=  len(lowerCamelCase_					) if isinstance(lowerCamelCase_					,							lowerCamelCase_					) else 1

													# get prompt text embeddings
													a_ :		Optional[int]							=  self.tokenizer(
													    lowerCamelCase_					,							padding='max_length'					,							max_length=self.tokenizer.model_max_length					,							return_tensors='pt'					,							)
													a_ :		Optional[int]							=  text_inputs.input_ids

													if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																				a_ :		List[str]							=  self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]					)
																				logger.warning(
																				    'The following part of your input was truncated because CLIP can only handle sequences up to'
																				    F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""					)
																				a_ :		Any							=  text_input_ids[:, : self.tokenizer.model_max_length]
													a_ :		List[Any]							=  self.text_encoder(text_input_ids.to(self.device					)					)[0]

													# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
													# While CLIP does normalize the pooled output of the text transformer when combining
													# the image and text embeddings, CLIP does not directly normalize the last hidden state.
													#
													# CLIP normalizing the pooled output.
													# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
													a_ :		Dict							=  prompt_embeds / prompt_embeds.norm(dim=-1					,							keepdim=lowerCamelCase_					)

													# duplicate text embeddings for each generation per prompt
													a_ :		Optional[int]							=  prompt_embeds.repeat_interleave(lowerCamelCase_					,							dim=0					)

													if do_classifier_free_guidance:
																				if self.learned_classifier_free_sampling_embeddings.learnable:
																											a_ :		str							=  self.learned_classifier_free_sampling_embeddings.embeddings
																											a_ :		str							=  negative_prompt_embeds.unsqueeze(0					).repeat(lowerCamelCase_					,							1					,							1					)
																				else:
																											a_ :		Dict							=  [""""""] * batch_size

																											a_ :		Tuple							=  text_input_ids.shape[-1]
																											a_ :		Optional[Any]							=  self.tokenizer(
																											    lowerCamelCase_					,							padding='max_length'					,							max_length=lowerCamelCase_					,							truncation=lowerCamelCase_					,							return_tensors='pt'					,							)
																											a_ :		str							=  self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]
																											# See comment for normalizing text embeddings
																											a_ :		Optional[int]							=  negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1					,							keepdim=lowerCamelCase_					)

																				# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																				a_ :		List[str]							=  negative_prompt_embeds.shape[1]
																				a_ :		Optional[Any]							=  negative_prompt_embeds.repeat(1					,							lowerCamelCase_					,							1					)
																				a_ :		List[str]							=  negative_prompt_embeds.view(batch_size * num_images_per_prompt					,							lowerCamelCase_					,							-1					)

																				# For classifier free guidance, we need to do two forward passes.
																				# Here we concatenate the unconditional and text embeddings into a single batch
																				# to avoid doing two forward passes
																				a_ :		Optional[Any]							=  torch.cat([negative_prompt_embeds, prompt_embeds]					)

													return prompt_embeds
						@torch.no_grad()
						def __call__(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	Union[str, List[str]]					,							SCREAMING_SNAKE_CASE__    :	int = 1_0_0					,							SCREAMING_SNAKE_CASE__    :	float = 5.0					,							SCREAMING_SNAKE_CASE__    :	float = 1.0					,							SCREAMING_SNAKE_CASE__    :	int = 1					,							SCREAMING_SNAKE_CASE__    :	Optional[Union[torch.Generator, List[torch.Generator]]] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[torch.FloatTensor] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[str] = "pil"					,							SCREAMING_SNAKE_CASE__    :	bool = True					,							SCREAMING_SNAKE_CASE__    :	Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,							SCREAMING_SNAKE_CASE__    :	int = 1					,							)		->      Union[ImagePipelineOutput, Tuple]:
													if isinstance(lowerCamelCase_					,							lowerCamelCase_					):
																				a_ :		Tuple							=  1
													elif isinstance(lowerCamelCase_					,							lowerCamelCase_					):
																				a_ :		Optional[Any]							=  len(lowerCamelCase_					)
													else:
																				raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase_					)}"""					)

													a_ :		Any							=  batch_size * num_images_per_prompt

													a_ :		Optional[Any]							=  guidance_scale > 1.0

													a_ :		int							=  self._encode_prompt(lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_					)

													if (callback_steps is None) or (
													    callback_steps is not None and (not isinstance(lowerCamelCase_					,							lowerCamelCase_					) or callback_steps <= 0)
													):
																				raise ValueError(
																				    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
																				    F""" {type(lowerCamelCase_					)}."""					)

													# get the initial completely masked latents unless the user supplied it

													a_ :		Tuple							=  (batch_size, self.transformer.num_latent_pixels)
													if latents is None:
																				a_ :		Optional[int]							=  self.transformer.num_vector_embeds - 1
																				a_ :		Tuple							=  torch.full(lowerCamelCase_					,							lowerCamelCase_					).to(self.device					)
													else:
																				if latents.shape != latents_shape:
																											raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""					)
																				if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
																											raise ValueError(
																											    'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
																											    F""" {self.transformer.num_vector_embeds - 1} (inclusive)."""					)
																				a_ :		Any							=  latents.to(self.device					)

													# set timesteps
													self.scheduler.set_timesteps(lowerCamelCase_					,							device=self.device					)

													a_ :		Optional[int]							=  self.scheduler.timesteps.to(self.device					)

													a_ :		str							=  latents

													for i, t in enumerate(self.progress_bar(lowerCamelCase_					)					):
																				# expand the sample if we are doing classifier free guidance
																				a_ :		Any							=  torch.cat([sample] * 2					) if do_classifier_free_guidance else sample

																				# predict the un-noised image
																				# model_output == `log_p_x_0`
																				a_ :		Optional[Any]							=  self.transformer(lowerCamelCase_					,							encoder_hidden_states=lowerCamelCase_					,							timestep=lowerCamelCase_					).sample

																				if do_classifier_free_guidance:
																											a_ :		Any							=  model_output.chunk(2					)
																											a_ :		Dict							=  model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																											model_output -= torch.logsumexp(lowerCamelCase_					,							dim=1					,							keepdim=lowerCamelCase_					)

																				a_ :		Any							=  self.truncate(lowerCamelCase_					,							lowerCamelCase_					)

																				# remove `log(0)`'s (`-inf`s)
																				a_ :		List[Any]							=  model_output.clamp(-7_0					)

																				# compute the previous noisy sample x_t -> x_t-1
																				a_ :		Dict							=  self.scheduler.step(lowerCamelCase_					,							timestep=lowerCamelCase_					,							sample=lowerCamelCase_					,							generator=lowerCamelCase_					).prev_sample

																				# call the callback, if provided
																				if callback is not None and i % callback_steps == 0:
																											callback(lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_					)

													a_ :		Optional[Any]							=  self.vqvae.config.vq_embed_dim
													a_ :		str							=  (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
													a_ :		Any							=  self.vqvae.quantize.get_codebook_entry(lowerCamelCase_					,							shape=lowerCamelCase_					)
													a_ :		Union[str, Any]							=  self.vqvae.decode(lowerCamelCase_					,							force_not_quantize=lowerCamelCase_					).sample

													a_ :		Optional[int]							=  (image / 2 + 0.5).clamp(0					,							1					)
													a_ :		str							=  image.cpu().permute(0					,							2					,							3					,							1					).numpy()

													if output_type == "pil":
																				a_ :		Tuple							=  self.numpy_to_pil(lowerCamelCase_					)

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=lowerCamelCase_					)

						def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	torch.FloatTensor					,							SCREAMING_SNAKE_CASE__    :	float					)		->      torch.FloatTensor:
													a_ :		Optional[int]							=  torch.sort(lowerCamelCase_					,							1					,							descending=lowerCamelCase_					)
													a_ :		Any							=  torch.exp(lowerCamelCase_					)
													a_ :		Union[str, Any]							=  sorted_p_x_0.cumsum(dim=1					) < truncation_rate

													# Ensure that at least the largest probability is not zeroed out
													a_ :		Any							=  torch.full_like(keep_mask[:, 0:1, :]					,							lowerCamelCase_					)
													a_ :		Any							=  torch.cat((all_true, keep_mask)					,							dim=1					)
													a_ :		Tuple							=  keep_mask[:, :-1, :]

													a_ :		Dict							=  keep_mask.gather(1					,							indices.argsort(1					)					)

													a_ :		List[Any]							=  log_p_x_0.clone()

													a_ :		Optional[int]							=  -torch.inf  # -inf = log(0)

													return rv






import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


UpperCAmelCase_				:			str   =	[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-classification',
        'language-modeling',
        'summarization',
        'token-classification',
        'question-answering',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
   import run_clm_flax
   import run_flax_glue
   import run_flax_ner
   import run_mlm_flax
   import run_qa
   import run_summarization_flax
   import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase_				:			Optional[int]   =	logging.getLogger()







def 	SCREAMING_SNAKE_CASE_						(      )     ->		Any:

       """simple docstring"""

       a_ :		Optional[int]							=  argparse.ArgumentParser()
       parser.add_argument('-f'    )
       a_ :		Optional[Any]							=  parser.parse_args()
       return args.f







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]  ,   __A			:      List[Any]="eval"    )     ->		Optional[int]:

       """simple docstring"""

       a_ :		List[Any]							=  os.path.join(__A  ,   F"""{split}_results.json"""    )
       if os.path.exists(__A    ):
              with open(__A  ,   'r'    ) as f:
                     return json.load(__A    )
       raise ValueError(F"""can't find {path}"""    )


UpperCAmelCase_				:			str   =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Tuple:
             a_ :		Optional[Any]							=  self.get_auto_remove_tmp_dir()
             a_ :		List[str]							=  F"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_flax_glue.main()
                    a_ :		str							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.75					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      str:
             a_ :		List[str]							=  self.get_auto_remove_tmp_dir()
             a_ :		Union[str, Any]							=  F"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_clm_flax.main()
                    a_ :		List[Any]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertLess(result['eval_perplexity']					,							1_0_0					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      int:
             a_ :		Tuple							=  self.get_auto_remove_tmp_dir()
             a_ :		Dict							=  F"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_summarization_flax.main()
                    a_ :		List[str]							=  get_results(SCREAMING_SNAKE_CASE__					,							split='test'					)
                    self.assertGreaterEqual(result['test_rouge1']					,							1_0					)
                    self.assertGreaterEqual(result['test_rouge2']					,							2					)
                    self.assertGreaterEqual(result['test_rougeL']					,							7					)
                    self.assertGreaterEqual(result['test_rougeLsum']					,							7					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Optional[Any]:
             a_ :		int							=  self.get_auto_remove_tmp_dir()
             a_ :		Dict							=  F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_mlm_flax.main()
                    a_ :		List[Any]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertLess(result['eval_perplexity']					,							4_2					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      List[str]:
             a_ :		str							=  self.get_auto_remove_tmp_dir()
             a_ :		List[str]							=  F"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_ta_mlm_flax.main()
                    a_ :		Dict							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.42					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      List[str]:
             # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
             a_ :		int							=  7 if get_gpu_count() > 1 else 2

             a_ :		Dict							=  self.get_auto_remove_tmp_dir()
             a_ :		Tuple							=  F"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_flax_ner.main()
                    a_ :		List[str]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.75					)
                    self.assertGreaterEqual(result['eval_f1']					,							0.3					)

      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      int:
             a_ :		List[str]							=  self.get_auto_remove_tmp_dir()
             a_ :		int							=  F"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

             with patch.object(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_qa.main()
                    a_ :		str							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_f1']					,							3_0					)
                    self.assertGreaterEqual(result['eval_exact']					,							3_0					)


