



import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
 import os

 from flax.core.frozen_dict import unfreeze
 from flax.traverse_util import flatten_dict

 from transformers import FlaxBertModel

 __snake_case						:str						=					'''0.12'''  # assumed parallelism: 8

@require_flax
@is_staging_test
class    _A	(     unittest.TestCase   ):



       @classmethod
       def 		_lowerCamelCase      (		cls							:					Optional[Any]):


           '''simple docstring'''

           __a							=    TOKEN
           HfFolder.save_token(__SCREAMING_SNAKE_CASE)



       @classmethod
       def 		_lowerCamelCase      (		cls							:					Union[str, Any]):


           '''simple docstring'''

           try:
               delete_repo(token=cls._token					,		repo_id='''test-model-flax''')
           except HTTPError:
               pass

           try:
               delete_repo(token=cls._token					,		repo_id='''valid_org/test-model-flax-org''')
           except HTTPError:
               pass



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    BertConfig(
               vocab_size=99					,		hidden_size=32					,		num_hidden_layers=5					,		num_attention_heads=4					,		intermediate_size=37)
           __a							=    FlaxBertModel(__SCREAMING_SNAKE_CASE)
           model.push_to_hub('''test-model-flax'''					,		use_auth_token=self._token)

           __a							=    FlaxBertModel.from_pretrained(F'{USER}/test-model-flax')

           __a							=    flatten_dict(unfreeze(model.params))
           __a							=    flatten_dict(unfreeze(new_model.params))

           for key in base_params.keys():
               __a							=    (base_params[key] - new_params[key]).sum().item()
               self.assertLessEqual(__SCREAMING_SNAKE_CASE					,		1E-3					,		msg=F'{key} not identical')

           # Reset repo
           delete_repo(token=self._token					,		repo_id='''test-model-flax''')

           # Push to hub via save_pretrained
           with tempfile.TemporaryDirectory() as tmp_dir:
               model.save_pretrained(__SCREAMING_SNAKE_CASE					,		repo_id='''test-model-flax'''					,		push_to_hub=__SCREAMING_SNAKE_CASE					,		use_auth_token=self._token)

           __a							=    FlaxBertModel.from_pretrained(F'{USER}/test-model-flax')

           __a							=    flatten_dict(unfreeze(model.params))
           __a							=    flatten_dict(unfreeze(new_model.params))

           for key in base_params.keys():
               __a							=    (base_params[key] - new_params[key]).sum().item()
               self.assertLessEqual(__SCREAMING_SNAKE_CASE					,		1E-3					,		msg=F'{key} not identical')





       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    BertConfig(
               vocab_size=99					,		hidden_size=32					,		num_hidden_layers=5					,		num_attention_heads=4					,		intermediate_size=37)
           __a							=    FlaxBertModel(__SCREAMING_SNAKE_CASE)
           model.push_to_hub('''valid_org/test-model-flax-org'''					,		use_auth_token=self._token)

           __a							=    FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''')

           __a							=    flatten_dict(unfreeze(model.params))
           __a							=    flatten_dict(unfreeze(new_model.params))

           for key in base_params.keys():
               __a							=    (base_params[key] - new_params[key]).sum().item()
               self.assertLessEqual(__SCREAMING_SNAKE_CASE					,		1E-3					,		msg=F'{key} not identical')

           # Reset repo
           delete_repo(token=self._token					,		repo_id='''valid_org/test-model-flax-org''')

           # Push to hub via save_pretrained
           with tempfile.TemporaryDirectory() as tmp_dir:
               model.save_pretrained(
                   __SCREAMING_SNAKE_CASE					,		repo_id='''valid_org/test-model-flax-org'''					,		push_to_hub=__SCREAMING_SNAKE_CASE					,		use_auth_token=self._token)

           __a							=    FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''')

           __a							=    flatten_dict(unfreeze(model.params))
           __a							=    flatten_dict(unfreeze(new_model.params))

           for key in base_params.keys():
               __a							=    (base_params[key] - new_params[key]).sum().item()
               self.assertLessEqual(__SCREAMING_SNAKE_CASE					,		1E-3					,		msg=F'{key} not identical')





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    True
    __a							=    flatten_dict(modela.params    )
    __a							=    flatten_dict(modela.params    )
    for key in flat_params_a.keys():
        if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]    )    ) > 1E-4:
            __a							=    False

    return models_are_equal

@require_flax
class    _A	(     unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''')
           __a							=    FlaxBertModel(__SCREAMING_SNAKE_CASE)

           __a							=    '''bert'''
           with tempfile.TemporaryDirectory() as tmp_dir:
               model.save_pretrained(os.path.join(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE))

               with self.assertRaises(__SCREAMING_SNAKE_CASE):
                   __a							=    FlaxBertModel.from_pretrained(__SCREAMING_SNAKE_CASE)

               __a							=    FlaxBertModel.from_pretrained(__SCREAMING_SNAKE_CASE					,		subfolder=__SCREAMING_SNAKE_CASE)

           self.assertTrue(check_models_equal(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''')
           __a							=    FlaxBertModel(__SCREAMING_SNAKE_CASE)

           __a							=    '''bert'''
           with tempfile.TemporaryDirectory() as tmp_dir:
               model.save_pretrained(os.path.join(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)					,		max_shard_size='''10KB''')

               with self.assertRaises(__SCREAMING_SNAKE_CASE):
                   __a							=    FlaxBertModel.from_pretrained(__SCREAMING_SNAKE_CASE)

               __a							=    FlaxBertModel.from_pretrained(__SCREAMING_SNAKE_CASE					,		subfolder=__SCREAMING_SNAKE_CASE)

           self.assertTrue(check_models_equal(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    '''bert'''
           __a							=    '''hf-internal-testing/tiny-random-bert-subfolder'''

           with self.assertRaises(__SCREAMING_SNAKE_CASE):
               __a							=    FlaxBertModel.from_pretrained(__SCREAMING_SNAKE_CASE)

           __a							=    FlaxBertModel.from_pretrained(__SCREAMING_SNAKE_CASE					,		subfolder=__SCREAMING_SNAKE_CASE)

           self.assertIsNotNone(__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    '''bert'''
           __a							=    '''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
           with self.assertRaises(__SCREAMING_SNAKE_CASE):
               __a							=    FlaxBertModel.from_pretrained(__SCREAMING_SNAKE_CASE)

           __a							=    FlaxBertModel.from_pretrained(__SCREAMING_SNAKE_CASE					,		subfolder=__SCREAMING_SNAKE_CASE)

           self.assertIsNotNone(__SCREAMING_SNAKE_CASE)










from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

__snake_case						:Dict						=					logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase   )
class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					int					,		*__SCREAMING_SNAKE_CASE							:					Optional[int]					,		**__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           super().__init__(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           requires_backends(self					,		'''vision''')
           self.check_model_type(
               TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING)



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					int=None					,		__SCREAMING_SNAKE_CASE							:					int=None					,		__SCREAMING_SNAKE_CASE							:					Tuple=None):


           '''simple docstring'''

           __a							=    {}
           __a							=    {}

           if prompt is not None:
               __a							=    prompt

           if generate_kwargs is not None:
               __a							=    generate_kwargs
           if max_new_tokens is not None:
               if "generate_kwargs" not in forward_kwargs:
                   __a							=    {}
               if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
                   raise ValueError(
                       '''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
                       ''' please use only one''')
               __a							=    max_new_tokens
           return preprocess_params, forward_kwargs, {}



       def __call__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Union[str, List[str], "Image.Image", List["Image.Image"]]					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           return super().__call__(__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=None):


           '''simple docstring'''

           __a							=    load_image(__SCREAMING_SNAKE_CASE)

           if prompt is not None:
               if not isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
                   raise ValueError(
                       F'Received an invalid text input, got - {type(__SCREAMING_SNAKE_CASE)} - but expected a single string. '
                       '''Note also that one single text can be provided for conditional image to text generation.''')

               __a							=    self.model.config.model_type

               if model_type == "git":
                   __a							=    self.image_processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)
                   __a							=    self.tokenizer(text=__SCREAMING_SNAKE_CASE					,		add_special_tokens=__SCREAMING_SNAKE_CASE).input_ids
                   __a							=    [self.tokenizer.cls_token_id] + input_ids
                   __a							=    torch.tensor(__SCREAMING_SNAKE_CASE).unsqueeze(0)
                   model_inputs.update({'''input_ids''': input_ids})

               elif model_type == "pix2struct":
                   __a							=    self.image_processor(images=__SCREAMING_SNAKE_CASE					,		header_text=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)

               elif model_type != "vision-encoder-decoder":
                   # vision-encoder-decoder does not support conditional generation
                   __a							=    self.image_processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)
                   __a							=    self.tokenizer(__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)
                   model_inputs.update(__SCREAMING_SNAKE_CASE)

               else:
                   raise ValueError(F'Model type {model_type} does not support conditional text generation')

           else:
               __a							=    self.image_processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors=self.framework)

           if self.model.config.model_type == "git" and prompt is None:
               __a							=    None

           return model_inputs



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Tuple=None):


           '''simple docstring'''

           if (
               "input_ids" in model_inputs
               and isinstance(model_inputs['''input_ids''']					,		__SCREAMING_SNAKE_CASE)
               and all(x is None for x in model_inputs['''input_ids'''])
           ):
               __a							=    None

           if generate_kwargs is None:
               __a							=    {}
           # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
           #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
           #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
           #  in the `_prepare_model_inputs` method.
           __a							=    model_inputs.pop(self.model.main_input_name)
           __a							=    self.model.generate(__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           return model_outputs





       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    []
           for output_ids in model_outputs:
               __a							=    {
                   '''generated_text''': self.tokenizer.decode(
                       __SCREAMING_SNAKE_CASE					,		skip_special_tokens=__SCREAMING_SNAKE_CASE					,		)
               }
               records.append(__SCREAMING_SNAKE_CASE)
           return records





