






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase      :       List[Any]    =						logging.get_logger(__name__)

lowerCamelCase      :       Optional[int]    =						{
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				"""pix2struct_text_model"""
      snake_case_	=				["""past_key_values"""]
      snake_case_	=				{
          """hidden_size""": """hidden_size""",
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }


      def __init__(						self       ,    __UpperCamelCase=5_0_2_4_4       ,    __UpperCamelCase=7_6_8       ,    __UpperCamelCase=6_4       ,    __UpperCamelCase=2_0_4_8       ,    __UpperCamelCase=1_2       ,    __UpperCamelCase=1_2       ,    __UpperCamelCase=3_2       ,    __UpperCamelCase=1_2_8       ,    __UpperCamelCase=0.1       ,    __UpperCamelCase=1e-6       ,    __UpperCamelCase=1.0       ,    __UpperCamelCase="gelu_new"       ,    __UpperCamelCase=0       ,    __UpperCamelCase=False       ,    __UpperCamelCase=0       ,    __UpperCamelCase=1       ,    __UpperCamelCase=False       ,    __UpperCamelCase=True       ,    **__UpperCamelCase       ,    )->						Optional[int]:
           __lowerCAmelCase             =       vocab_size
           __lowerCAmelCase             =       hidden_size
           __lowerCAmelCase             =       d_kv
           __lowerCAmelCase             =       d_ff
           __lowerCAmelCase             =       num_layers
           __lowerCAmelCase             =       num_heads
           __lowerCAmelCase             =       relative_attention_num_buckets
           __lowerCAmelCase             =       relative_attention_max_distance
           __lowerCAmelCase             =       dropout_rate
           __lowerCAmelCase             =       layer_norm_epsilon
           __lowerCAmelCase             =       initializer_factor
           __lowerCAmelCase             =       use_cache

           __lowerCAmelCase             =       eos_token_id
           __lowerCAmelCase             =       decoder_start_token_id

           # for backwards compatibility
           __lowerCAmelCase             =       dense_act_fn

           super().__init__(
               pad_token_id=__UpperCamelCase       ,    eos_token_id=__UpperCamelCase       ,    decoder_start_token_id=__UpperCamelCase       ,    tie_word_embeddings=__UpperCamelCase       ,    is_decoder=__UpperCamelCase       ,    **__UpperCamelCase       ,    )


      @classmethod
      def 			__UpperCAmelCase		(						cls       ,    __UpperCamelCase       ,    **__UpperCamelCase						)->						"PretrainedConfig":
           cls._set_token_in_kwargs(__UpperCamelCase						)

           __lowerCAmelCase      ,			__lowerCAmelCase             =       cls.get_config_dict(__UpperCamelCase       ,    **__UpperCamelCase						)

           # get the text config dict if we are loading from Pix2StructConfig
           if config_dict.get("model_type"						) == "pix2struct":
                __lowerCAmelCase             =       config_dict["text_config"]

           if "model_type" in config_dict and hasattr(cls       ,    "model_type"						) and config_dict["model_type"] != cls.model_type:
                logger.warning(
                    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""						)

           return cls.from_dict(__UpperCamelCase       ,    **__UpperCamelCase						)



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				"""pix2struct_vision_model"""


      def __init__(						self       ,    __UpperCamelCase=7_6_8       ,    __UpperCamelCase=7_6_8       ,    __UpperCamelCase=2_0_4_8       ,    __UpperCamelCase=6_4       ,    __UpperCamelCase=1_2       ,    __UpperCamelCase=1_2       ,    __UpperCamelCase="gelu_new"       ,    __UpperCamelCase=1e-6       ,    __UpperCamelCase=0.0       ,    __UpperCamelCase=0.0       ,    __UpperCamelCase=1e-10       ,    __UpperCamelCase=1.0       ,    __UpperCamelCase=4_0_9_6       ,    __UpperCamelCase=3_2       ,    __UpperCamelCase=1_2_8       ,    **__UpperCamelCase       ,    )->						Dict:
           super().__init__(**__UpperCamelCase						)

           __lowerCAmelCase             =       hidden_size
           __lowerCAmelCase             =       patch_embed_hidden_size
           __lowerCAmelCase             =       d_ff
           __lowerCAmelCase             =       dropout_rate
           __lowerCAmelCase             =       num_hidden_layers
           __lowerCAmelCase             =       num_attention_heads
           __lowerCAmelCase             =       initializer_range
           __lowerCAmelCase             =       initializer_factor
           __lowerCAmelCase             =       attention_dropout
           __lowerCAmelCase             =       layer_norm_eps
           __lowerCAmelCase             =       dense_act_fn
           __lowerCAmelCase             =       seq_len
           __lowerCAmelCase             =       relative_attention_num_buckets
           __lowerCAmelCase             =       relative_attention_max_distance
           __lowerCAmelCase             =       d_kv


      @classmethod
      def 			__UpperCAmelCase		(						cls       ,    __UpperCamelCase       ,    **__UpperCamelCase						)->						"PretrainedConfig":
           cls._set_token_in_kwargs(__UpperCamelCase						)

           __lowerCAmelCase      ,			__lowerCAmelCase             =       cls.get_config_dict(__UpperCamelCase       ,    **__UpperCamelCase						)

           # get the vision config dict if we are loading from Pix2StructConfig
           if config_dict.get("model_type"						) == "pix2struct":
                __lowerCAmelCase             =       config_dict["vision_config"]

           if "model_type" in config_dict and hasattr(cls       ,    "model_type"						) and config_dict["model_type"] != cls.model_type:
                logger.warning(
                    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""						)

           return cls.from_dict(__UpperCamelCase       ,    **__UpperCamelCase						)



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				"""pix2struct"""
      snake_case_	=				True


      def __init__(						self       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None       ,    __UpperCamelCase=1.0       ,    __UpperCamelCase=0.0_2       ,    __UpperCamelCase=False       ,    __UpperCamelCase=False       ,    __UpperCamelCase=True       ,    **__UpperCamelCase       ,    )->						List[Any]:
           super().__init__(tie_word_embeddings=__UpperCamelCase       ,    is_encoder_decoder=__UpperCamelCase       ,    **__UpperCamelCase						)

           if text_config is None:
                __lowerCAmelCase             =       {}
                logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values."						)

           if vision_config is None:
                __lowerCAmelCase             =       {}
                logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values."						)

           __lowerCAmelCase             =       PixaStructTextConfig(**__UpperCamelCase						)
           __lowerCAmelCase             =       PixaStructVisionConfig(**__UpperCamelCase						)

           __lowerCAmelCase             =       self.text_config.decoder_start_token_id
           __lowerCAmelCase             =       self.text_config.pad_token_id
           __lowerCAmelCase             =       self.text_config.eos_token_id

           __lowerCAmelCase             =       initializer_factor
           __lowerCAmelCase             =       initializer_range

           __lowerCAmelCase             =       self.initializer_range
           __lowerCAmelCase             =       self.initializer_range

           __lowerCAmelCase             =       is_vqa


      @classmethod
      def 			__UpperCAmelCase		(						cls       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						)->						Tuple:
           return cls(text_config=text_config.to_dict()       ,    vision_config=vision_config.to_dict()       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       copy.deepcopy(self.__dict__						)
           __lowerCAmelCase             =       self.text_config.to_dict()
           __lowerCAmelCase             =       self.vision_config.to_dict()
           __lowerCAmelCase             =       self.__class__.model_type
           return output














# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

lowerCamelCase      :       List[str]    =						'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
lowerCamelCase      :       str    =						[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
lowerCamelCase      :       List[Any]    =						dict(zip(vocab, range(len(vocab))))
lowerCamelCase      :       List[str]    =						['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
 lowerCamelCase      :       int    =						Path(tmpdirname)
 lowerCamelCase      :       Optional[int]    =						build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
 lowerCamelCase      :       List[str]    =						build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
 lowerCamelCase      :       int    =						build_dir / VOCAB_FILES_NAMES['''merges_file''']
 with open(src_vocab_file, '''w''') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(tgt_vocab_file, '''w''') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(merges_file, '''w''') as fp:
  fp.write('''\n'''.join(merges))

 lowerCamelCase      :       Dict    =						FSMTTokenizer(
     langs=['''en''', '''ru'''],
     src_vocab_size=len(vocab),
     tgt_vocab_size=len(vocab),
     src_vocab_file=src_vocab_file,
     tgt_vocab_file=tgt_vocab_file,
     merges_file=merges_file,
 )

lowerCamelCase      :       int    =						FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

lowerCamelCase      :       Any    =						FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
lowerCamelCase      :       int    =						tokenizer(['''Making tiny model'''], return_tensors='''pt''')
lowerCamelCase      :       Dict    =						tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru






