
import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin







class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Any       					=BlenderbotSmallTokenizer
							lowercase_       :       str       					=False




							def  A__      (				self):
												super().setUp()

												lowercase							=						['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
												lowercase							=						dict(zip(A__   ,range(len(A__))))

												lowercase							=						['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
												lowercase							=						{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

												lowercase							=						os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES['''vocab_file'''])
												lowercase							=						os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES['''merges_file'''])
												with open(self.vocab_file   ,'''w'''   ,encoding='''utf-8''') as fp:
																	fp.write(json.dumps(A__) + '''\n''')
												with open(self.merges_file   ,'''w'''   ,encoding='''utf-8''') as fp:
																	fp.write('''\n'''.join(A__))




							def  A__      (				self   ,**A__):
												kwargs.update(self.special_tokens_map)
												return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname   ,**A__)




							def  A__      (				self   ,A__):
												lowercase							=						'''adapt act apte'''
												lowercase							=						'''adapt act apte'''
												return input_text, output_text




							def  A__      (				self):
												lowercase							=						BlenderbotSmallTokenizer(self.vocab_file   ,self.merges_file   ,**self.special_tokens_map)
												lowercase							=						'''adapt act apte'''
												lowercase							=						['''adapt''', '''act''', '''ap@@''', '''te''']
												lowercase							=						tokenizer.tokenize(A__)
												self.assertListEqual(A__   ,A__)

												lowercase							=						[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

												lowercase							=						[0, 1, 2, 3, 4, 5]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(A__)   ,A__)




							def  A__      (				self):
												lowercase							=						BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''')
												assert tok('''sam''').input_ids == [1_3_8_4]
												lowercase							=						'''I am a small frog.'''
												lowercase							=						tok([src_text]   ,padding=A__   ,truncation=A__)['''input_ids''']
												lowercase							=						tok.batch_decode(A__   ,skip_special_tokens=A__   ,clean_up_tokenization_spaces=A__)[0]
												assert src_text != decoded  # I wish it did!
												assert decoded == "i am a small frog ."





							def  A__      (				self):
												lowercase							=						BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''')
												lowercase							=						'''I am a small frog .'''
												lowercase							=						'''.'''
												lowercase							=						tok(A__)['''input_ids''']
												lowercase							=						tok(A__)['''input_ids''']

												assert encoded[-1] == encoded_dot[0]






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

					lowercase							=						(
					    ('''layer.''', '''layer_'''),
					    ('''word_embeddings.weight''', '''word_embeddings'''),
					    ('''position_embeddings.weight''', '''position_embeddings'''),
					    ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
					    ('''.''', '''/'''),
					    ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
					    ('''LayerNorm/bias''', '''LayerNorm/beta'''),
					    ('''weight''', '''kernel'''),
					)

					if not os.path.isdir(lowerCAmelCase__		):
										os.makedirs(lowerCAmelCase__		)

					lowercase							=						model.state_dict()

					def to_tf_var_name(lowerCAmelCase__		):
										for patt, repl in iter(lowerCAmelCase__		):
															lowercase							=						name.replace(lowerCAmelCase__ ,			lowerCAmelCase__		)
										return f'bert/{name}'

					def create_tf_var(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
										lowercase							=						tf.dtypes.as_dtype(tensor.dtype		)
										lowercase							=						tf.get_variable(dtype=lowerCAmelCase__ ,			shape=tensor.shape ,			name=lowerCAmelCase__ ,			initializer=tf.zeros_initializer()		)
										session.run(tf.variables_initializer([tf_var]		)		)
										session.run(lowerCAmelCase__		)
										return tf_var

					tf.reset_default_graph()
					with tf.Session() as session:
										for var_name in state_dict:
															lowercase							=						to_tf_var_name(lowerCAmelCase__		)
															lowercase							=						state_dict[var_name].numpy()
															if any(x in var_name for x in tensors_to_transpose		):
																				lowercase							=						torch_tensor.T
															lowercase							=						create_tf_var(tensor=lowerCAmelCase__ ,			name=lowerCAmelCase__ ,			session=lowerCAmelCase__		)
															tf.keras.backend.set_value(lowerCAmelCase__ ,			lowerCAmelCase__		)
															lowercase							=						session.run(lowerCAmelCase__		)
															print(f'Successfully created {tf_name}: {np.allclose(lowerCAmelCase__ ,			lowerCAmelCase__		)}'		)

										lowercase							=						tf.train.Saver(tf.trainable_variables()		)
										saver.save(lowerCAmelCase__ ,			os.path.join(lowerCAmelCase__ ,			model_name.replace('''-''' ,			'''_'''		) + '''.ckpt'''		)		)



def        UpperCamelCase    (							lowerCAmelCase__=None		):
					'''simple docstring'''
					lowercase							=						argparse.ArgumentParser()
					parser.add_argument('''--model_name''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''model name e.g. bert-base-uncased'''		)
					parser.add_argument(
					    '''--cache_dir''' ,			type=lowerCAmelCase__ ,			default=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''Directory containing pytorch model'''		)
					parser.add_argument('''--pytorch_model_path''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''/path/to/<pytorch-model-name>.bin'''		)
					parser.add_argument('''--tf_cache_dir''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''Directory in which to save tensorflow model'''		)
					lowercase							=						parser.parse_args(lowerCAmelCase__		)

					lowercase							=						BertModel.from_pretrained(
					    pretrained_model_name_or_path=args.model_name ,			state_dict=torch.load(args.pytorch_model_path		) ,			cache_dir=args.cache_dir ,			)

					convert_pytorch_checkpoint_to_tf(model=lowerCAmelCase__ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name		)


if __name__ == "__main__":
							main()




