from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers.models.xglm.modeling_tf_xglm import (
					    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFXGLMForCausalLM,
					    TFXGLMModel,
					)






@require_tf
class 	A_		:
							_UpperCAmelCase   :  Optional[Any]										=				XGLMConfig
							_UpperCAmelCase   :  Tuple										=				{}
							_UpperCAmelCase   :  Dict										=				'''gelu'''
							def __init__(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      Any       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=1_4       ,SCREAMING_SNAKE_CASE__		:      Any=7       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=True       ,SCREAMING_SNAKE_CASE__		:      List[Any]=True       ,SCREAMING_SNAKE_CASE__		:      int=True       ,SCREAMING_SNAKE_CASE__		:      List[str]=9_9       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=3_2       ,SCREAMING_SNAKE_CASE__		:      str=2       ,SCREAMING_SNAKE_CASE__		:      str=4       ,SCREAMING_SNAKE_CASE__		:      int=3_7       ,SCREAMING_SNAKE_CASE__		:      List[str]="gelu"       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=0.1       ,SCREAMING_SNAKE_CASE__		:      Any=0.1       ,SCREAMING_SNAKE_CASE__		:      str=5_1_2       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=0.02       ,):
								__lowerCamelCase    :    Union[str, Any] 	=			parent
								__lowerCamelCase    :    str 	=			batch_size
								__lowerCamelCase    :    Optional[Any] 	=			seq_length
								__lowerCamelCase    :    Any 	=			is_training
								__lowerCamelCase    :    int 	=			use_input_mask
								__lowerCamelCase    :    List[str] 	=			use_labels
								__lowerCamelCase    :    List[Any] 	=			vocab_size
								__lowerCamelCase    :    int 	=			d_model
								__lowerCamelCase    :    Tuple 	=			num_hidden_layers
								__lowerCamelCase    :    List[Any] 	=			num_attention_heads
								__lowerCamelCase    :    List[Any] 	=			ffn_dim
								__lowerCamelCase    :    List[Any] 	=			activation_function
								__lowerCamelCase    :    Any 	=			activation_dropout
								__lowerCamelCase    :    Dict 	=			attention_dropout
								__lowerCamelCase    :    List[Any] 	=			max_position_embeddings
								__lowerCamelCase    :    Optional[Any] 	=			initializer_range
								__lowerCamelCase    :    int 	=			None
								__lowerCamelCase    :    Optional[Any] 	=			0
								__lowerCamelCase    :    Optional[Any] 	=			2
								__lowerCamelCase    :    int 	=			1
							def    lowerCAmelCase					(		self		:      Dict):
								return XGLMConfig.from_pretrained('facebook/xglm-564M')
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    Optional[int] 	=			tf.clip_by_value(
								    ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size)       ,clip_value_min=0       ,clip_value_max=3)

								__lowerCamelCase    :    List[Any] 	=			None
								if self.use_input_mask:
									__lowerCamelCase    :    str 	=			random_attention_mask([self.batch_size, self.seq_length])

								__lowerCamelCase    :    List[str] 	=			self.get_config()

								__lowerCamelCase    :    Tuple 	=			floats_tensor([self.num_hidden_layers, self.num_attention_heads]       ,2)

								return (
								    config,
								    input_ids,
								    input_mask,
								    head_mask,
								)
							def    lowerCAmelCase					(		self		:      Any):
								return XGLMConfig(
								    vocab_size=self.vocab_size       ,d_model=self.hidden_size       ,num_layers=self.num_hidden_layers       ,attention_heads=self.num_attention_heads       ,ffn_dim=self.ffn_dim       ,activation_function=self.activation_function       ,activation_dropout=self.activation_dropout       ,attention_dropout=self.attention_dropout       ,max_position_embeddings=self.max_position_embeddings       ,initializer_range=self.initializer_range       ,use_cache=SCREAMING_SNAKE_CASE__       ,bos_token_id=self.bos_token_id       ,eos_token_id=self.eos_token_id       ,pad_token_id=self.pad_token_id       ,return_dict=SCREAMING_SNAKE_CASE__       ,)


							def    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase    :    str 	=			self.prepare_config_and_inputs()

								(
								    __lowerCamelCase			
								)    :    List[str] 	=			config_and_inputs

								__lowerCamelCase    :    Any 	=			{
								    'input_ids': input_ids,
								    'head_mask': head_mask,
								}

								return config, inputs_dict






@require_tf
class 	A_		(	SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  List[Any]										=				(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
							_UpperCAmelCase   :  Any										=				(TFXGLMForCausalLM,) if is_tf_available() else ()
							_UpperCAmelCase   :  Optional[Any]										=				(
							    {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
							)
							_UpperCAmelCase   :  Dict										=				False
							_UpperCAmelCase   :  List[Any]										=				False
							_UpperCAmelCase   :  Any										=				False
							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    int 	=			TFXGLMModelTester(self)
								__lowerCamelCase    :    Dict 	=			ConfigTester(self       ,config_class=SCREAMING_SNAKE_CASE__       ,n_embd=3_7)
							def    lowerCAmelCase					(		self		:      Tuple):
								self.config_tester.run_common_tests()
							@slow
							def    lowerCAmelCase					(		self		:      List[Any]):
								for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase    :    int 	=			TFXGLMModel.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)


							@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.')
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								super().test_resize_token_embeddings()






@require_tf
class 	A_		(	unittest.TestCase    ):
							@slow
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Any=True):
								__lowerCamelCase    :    List[Any] 	=			TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M')
								__lowerCamelCase    :    str 	=			tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]]       ,dtype=tf.intaa)  # The dog
								# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
								# fmt: off
								__lowerCamelCase    :    Optional[Any] 	=			[2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
								# fmt: on
								__lowerCamelCase    :    List[str] 	=			model.generate(SCREAMING_SNAKE_CASE__       ,do_sample=SCREAMING_SNAKE_CASE__       ,num_beams=1)
								if verify_outputs:
									self.assertListEqual(output_ids[0].numpy().tolist()       ,SCREAMING_SNAKE_CASE__)
							@slow
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    Optional[Any] 	=			XGLMTokenizer.from_pretrained('facebook/xglm-564M')
								__lowerCamelCase    :    int 	=			TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M')

								tf.random.set_seed(0)
								__lowerCamelCase    :    Union[str, Any] 	=			tokenizer('Today is a nice day and'       ,return_tensors='tf')
								__lowerCamelCase    :    str 	=			tokenized.input_ids
								# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
								with tf.device(':/CPU:0'):
									__lowerCamelCase    :    Tuple 	=			model.generate(SCREAMING_SNAKE_CASE__       ,do_sample=SCREAMING_SNAKE_CASE__       ,seed=[7, 0])
								__lowerCamelCase    :    Union[str, Any] 	=			tokenizer.decode(output_ids[0]       ,skip_special_tokens=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Optional[Any] 	=			(
								    'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
								)
								self.assertEqual(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)


							@slow
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    int 	=			TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M')
								__lowerCamelCase    :    Optional[Any] 	=			XGLMTokenizer.from_pretrained('facebook/xglm-564M')

								__lowerCamelCase    :    Any 	=			'left'

								# use different length sentences to test batching
								__lowerCamelCase    :    List[Any] 	=			[
								    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
								    'left-padding, such as in batched generation. The output for the sequence below should be the same '
								    'regardless of whether left padding is applied or not. When',
								    'Hello, my dog is a little',
								]

								__lowerCamelCase    :    int 	=			tokenizer(SCREAMING_SNAKE_CASE__       ,return_tensors='tf'       ,padding=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			inputs['input_ids']

								__lowerCamelCase    :    Tuple 	=			model.generate(input_ids=SCREAMING_SNAKE_CASE__       ,attention_mask=inputs['attention_mask']       ,max_new_tokens=1_2)

								__lowerCamelCase    :    Optional[int] 	=			tokenizer(sentences[0]       ,return_tensors='tf').input_ids
								__lowerCamelCase    :    Union[str, Any] 	=			model.generate(input_ids=SCREAMING_SNAKE_CASE__       ,max_new_tokens=1_2)

								__lowerCamelCase    :    int 	=			tokenizer(sentences[1]       ,return_tensors='tf').input_ids
								__lowerCamelCase    :    Any 	=			model.generate(input_ids=SCREAMING_SNAKE_CASE__       ,max_new_tokens=1_2)

								__lowerCamelCase    :    List[Any] 	=			tokenizer.batch_decode(SCREAMING_SNAKE_CASE__       ,skip_special_tokens=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Dict 	=			tokenizer.decode(output_non_padded[0]       ,skip_special_tokens=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Tuple 	=			tokenizer.decode(output_padded[0]       ,skip_special_tokens=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    str 	=			[
								    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
								    'left-padding, such as in batched generation. The output for the sequence below should be the same '
								    'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
								    'a single',
								    'Hello, my dog is a little bit of a shy one, but he is very friendly',
								]
								self.assertListEqual(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								self.assertListEqual(SCREAMING_SNAKE_CASE__       ,[non_padded_sentence, padded_sentence])







import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


a		=logging.get_logger(__name__)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					None:
	__lowerCamelCase    :    Tuple 	=			nn.ModuleList([src_layers[i] for i in layers_to_copy]      )
	assert len(lowerCamelCase__      ) == len(lowerCamelCase__      ), F"{len(lowerCamelCase__      )} != {len(lowerCamelCase__      )}"
	dest_layers.load_state_dict(layers_to_copy.state_dict()      )


a		={
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
a		={
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					str:
	try:
		__lowerCamelCase    :    List[str] 	=			LAYERS_TO_COPY[n_teacher][n_student]
		return val
	except KeyError:
		if n_student != n_teacher:
			warnings.warn(
			    F"no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"
			    F" {n_student}"      )
		return list(range(lowerCamelCase__      )      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[int]:
	if n_student > n_teacher:
		raise ValueError(F"Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}"      )
	elif n_teacher == n_student:
		return list(range(lowerCamelCase__      )      )
	elif n_student == 1:
		return [n_teacher - 1]
	else:
		return LAYERS_TO_SUPERVISE[n_teacher][n_student]






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__ = "student"     ,			lowerCamelCase__ = None     ,			lowerCamelCase__ = None     ,			lowerCamelCase__=False     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None     ,			**lowerCamelCase__     ,			)  ->					Tuple[PreTrainedModel, List[int], List[int]]:
	__lowerCamelCase    :    int 	=			'encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'
	assert (e is not None) or (d is not None), _msg
	if isinstance(lowerCamelCase__     ,			lowerCamelCase__      ):
		AutoTokenizer.from_pretrained(lowerCamelCase__      ).save_pretrained(lowerCamelCase__      )  # purely for convenience
		__lowerCamelCase    :    Union[str, Any] 	=			AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__      ).eval()
	else:
		assert isinstance(lowerCamelCase__     ,			lowerCamelCase__      ), F"teacher must be a model or string got type {type(lowerCamelCase__      )}"
	__lowerCamelCase    :    str 	=			teacher.config.to_diff_dict()

	try:
		__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			teacher.config.encoder_layers, teacher.config.decoder_layers
		if e is None:
			__lowerCamelCase    :    Optional[int] 	=			teacher_e
		if d is None:
			__lowerCamelCase    :    Optional[Any] 	=			teacher_d
		init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}      )
	except AttributeError:  # T5
		if hasattr(teacher.config     ,			'num_encoder_layers'      ):
			__lowerCamelCase	,			__lowerCamelCase    :    int 	=			teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
		else:
			__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			teacher.config.num_layers, teacher.config.num_decoder_layers
		if e is None:
			__lowerCamelCase    :    Union[str, Any] 	=			teacher_e
		if d is None:
			__lowerCamelCase    :    Any 	=			teacher_d
		if hasattr(teacher.config     ,			'num_encoder_layers'      ):
			init_kwargs.update({'num_encoder_layers': e, 'num_decoder_layers': d}      )
		else:
			init_kwargs.update({'num_layers': e, 'num_decoder_layers': d}      )

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
	init_kwargs.update(lowerCamelCase__      )

	# Copy weights
	__lowerCamelCase    :    str 	=			teacher.config_class(**lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			AutoModelForSeqaSeqLM.from_config(lowerCamelCase__      )
	# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
	__lowerCamelCase    :    Tuple 	=			student.load_state_dict(teacher.state_dict()     ,			strict=lowerCamelCase__      )
	assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

	if copy_first_teacher_layers:  # Our copying is done. We just log and save
		__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			list(range(lowerCamelCase__      )      ), list(range(lowerCamelCase__      )      )
		logger.info(
		    F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"
		    F" {save_path}"      )
		student.save_pretrained(lowerCamelCase__      )
		return student, e_layers_to_copy, d_layers_to_copy

	# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
	if e_layers_to_copy is None:
		__lowerCamelCase    :    List[int] 	=			pick_layers_to_copy(lowerCamelCase__     ,			lowerCamelCase__      )
	if d_layers_to_copy is None:
		__lowerCamelCase    :    List[int] 	=			pick_layers_to_copy(lowerCamelCase__     ,			lowerCamelCase__      )

	try:
		if hasattr(
		    lowerCamelCase__     ,			'prophetnet'      ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
			copy_layers(teacher.prophetnet.encoder.layers     ,			student.prophetnet.encoder.layers     ,			lowerCamelCase__      )
			copy_layers(teacher.prophetnet.decoder.layers     ,			student.prophetnet.decoder.layers     ,			lowerCamelCase__      )
		else:
			copy_layers(teacher.model.encoder.layers     ,			student.model.encoder.layers     ,			lowerCamelCase__      )
			copy_layers(teacher.model.decoder.layers     ,			student.model.decoder.layers     ,			lowerCamelCase__      )
	except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
		copy_layers(teacher.encoder.block     ,			student.encoder.block     ,			lowerCamelCase__      )
		copy_layers(teacher.decoder.block     ,			student.decoder.block     ,			lowerCamelCase__      )
	logger.info(
	    F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"      )
	__lowerCamelCase    :    Dict 	=			{
	    'teacher_type': teacher.config.model_type,
	    'copied_encoder_layers': e_layers_to_copy,
	    'copied_decoder_layers': d_layers_to_copy,
	}
	student.save_pretrained(lowerCamelCase__      )
	# Save information about copying for easier reproducibility

	return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
					fire.Fire(create_student_by_copying_alternating_layers)




